Essays about: "Deep Learning"

Showing result 1 - 5 of 1917 essays containing the words Deep Learning.

  1. 1. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods

    University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

    Author : Nikolaos Staikos; [2024]
    Keywords : Physical Geography; Ecosystem Analysis; Bike-sharing demand; Machine learning; Deep learning; Spatial regression; Graph Convolutional Neural Network; Multiple Linear Regression; Multilayer Perceptron Regressor; Support Vector Machine; Random Forest Regressor; Urban environment; Micro-mobility; Station planning; Geomatics; Earth and Environmental Sciences;

    Abstract : Public Bike-Sharing systems have been employed in many cities around the globe. Shared bikes are an efficient and convenient means of transportation in advanced societies. Nonetheless, station planning and local bike-sharing network effectiveness can be challenging. READ MORE

  2. 2. ML implementation for analyzing and estimating product prices

    University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Author : Abel Getachew Kenea; Gabriel Fagerslett; [2024]
    Keywords : Machine Learning; ML; Regression; Deep Learning; Artificial Neural Network; ANN; TensorFlow; ScikitLearn; CUDA; cuDNN; Estimation; Prediction; AI; Artificial Intelligence; Price Tracking; Price Logging; Price Estimation; Supervised Learning; Random Forest; Decision Trees; Batch Learning; Hyperparameter Tuning; Linear Regression; Multiple Linear Regression; Maskininlärning; Djup lärning; Artificiellt Neuralt Nätverk; Regression; TensorFlow; SciktLearn; ML; ANN; Estimation; Uppskattning; CUDA; cuDNN; AI; Artificiell Intelligens; pris loggning; pris estimation; prisspårning; Batchinlärning; Hyperparameterjustering; Linjär Regression; Multipel Linjär Regression; Supervised Learning; Random Forest; Decision Trees;

    Abstract : Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. READ MORE

  3. 3. Attack Strategies in Federated Learning for Regression Models : A Comparative Analysis with Classification Models

    University essay from Umeå universitet/Institutionen för datavetenskap

    Author : Sofia Leksell; [2024]
    Keywords : Federated Learning; Adversarial Attacks; Regression; Classification;

    Abstract : Federated Learning (FL) has emerged as a promising approach for decentralized model training across multiple devices, while still preserving data privacy. Previous research has predominantly concentrated on classification tasks in FL settings, leaving  a noticeable gap in FL research specifically for regression models. READ MORE

  4. 4. Learning a Grasp Prediction Model for Forestry Applications

    University essay from Umeå universitet/Institutionen för fysik

    Author : Elias Olofsson; [2024]
    Keywords : Forwarder; Autonomous grasping; Deep learning; Multibody dynamics; Convolutional neural network;

    Abstract : Since the advent of machine learning and machine vision methods, progress has been made in tackling the long-standing research question of autonomous grasping of arbitrary objects using robotic end-effectors. Building on these efforts, we focus on a subset of the general grasping problem concerning the automation of a forwarder. READ MORE

  5. 5. Evaluation of Deep Q-Learning Applied to City Environment Autonomous Driving

    University essay from Uppsala universitet/Signaler och system

    Author : Jonas Wedén; [2024]
    Keywords : Machine Learning; ML; Reinforcement Learning; RL; Neural Network; Deep Learning; Autonomous Vehicle; Vehicle; CARLA; Convolutional Neural Network; CNN; Precisit; Q-learning; Deep Q-learning; DQN;

    Abstract : This project’s goal was to assess both the challenges of implementing the Deep Q-Learning algorithm to create an autonomous car in the CARLA simulator, and the driving performance of the resulting model. An agent was trained to follow waypoints based on two main approaches. READ MORE